A TIME‐SERIES APPROACH FOR CONSTRUCTING EXPECTATIONS MODELS*

Abstract

ABSTRACTThis article presents an efficient way of dealing with adaptive expectations models—a way that makes use of all the information available in the data. The procedure is based on multiple‐input transfer functions (MITFs): by calculating lead and lag cross correlations between innovations associated with the variables in the model, it is possible to determine which periods have the greatest effects on the dependent variable. If information about k periods ahead is required, fitted values for the expectation variables are used to generate k‐period‐ahead forecasts. These in turn can be used in the estimation of the transfer function equation, which not only contains the usual lagged variables but also allows for incorporation of lead‐fitted values for the expectation variables. The MITF identification and estimation procedures used are based on the corner method. The method is contrasted with the Almon distributed‐lag approach using a model relating stock market prices to interest rates and expected corporate profits.